1. Field of the Invention
The present invention relates generally to a codebook-based beamforming system, and, in particular, to an apparatus and method for determining a transmission beamforming vector, taking a time-variant channel into account in a codebook-based Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) communication system.
2. Description of the Related Art
A MIMO system with a plurality of transmit antennas and a plurality of receive antennas provides spatial diversity that mitigates the signal level variation of a fading channel. A narrow-band MIMO system can acquire a diversity gain as high as the product of the numbers of transmit and receive antennas. Without knowledge of Channel State Information (CSI), a transmitter achieves diversity gain using a variety of space-time codes. If the transmitter has knowledge of CSI, it acquires spatial diversity gain by simple transmission beamforming and reception combining.
With knowledge of CSI, array gain as well diversity gain of the same order as a space-time code can be achieved, thereby remarkably increasing system performance. Yet, the transmission beamforming technique is viable only if the transmitter has knowledge of a transmission beamforming vector. In a Frequency Division Duplexing (FDD) system using different channels on the downlink and uplink, a receiver should feed back the transmission beamforming vector to the transmitter.
The introduction of OFDM facilitates application of transmission beamforming techniques designed for narrow-band systems to broadband systems. Typically, a MIMO-OFDM system converts a broadband MIMO channel to a set of narrow-band MIMO channels. Each narrow-band MIMO channel corresponds to a subcarrier. The use of a transmission beamforming technique in the MIMO-OFDM system requires feedback of transmission beamforming vectors from the receiver to the transmitter.
In general, a very large number of subcarriers are used and thus a large amount of feedback information is sent. Although it is possible to decrease the feedback to some degree by limited feedback schemes proposed for the narrow-band MIMO system, the reduction of feedback is limited because of too large of a total number of subcarriers.
As a solution to the problem, a technique for interpolating transmission beamforming vectors has been proposed. Based on the fact that there is a relation between optimum transmission beamforming vectors of adjacent subcarriers, the receiver sends information about the transmission beamforming vectors of some of total subcarriers to the transmitter and the transmitter determines the transmission beamforming vectors of the other subcarriers by interpolating the received transmission beamforming vectors. A combined use of the interpolation technique and a codebook-based transmission beamforming technique can reduce feedback significantly in a broadband MIMO-OFDM transmission beamforming system.
Despite its ability to reduce feedback for a broadband channel, the conventional interpolation technique has drawbacks concerning the time-variant properties of the channel. In most mobile communication environments, channels vary over time and thus transmission beamforming vectors also change over time at the transmitter. Hence, the receiver has to continuously notify the transmitter of transmission beamforming vectors according to channel changes, requiring a large amount of feedback. Moreover, if the system has a processing delay, that is, if the transmission beamforming vectors from the receiver cannot be used immediately due to some factors like detection delay at the transmitter, the channel may change during the processing delay. As a consequence, even if the receiver optimizes the transmission beamforming vectors, they are not optimal at the transmitter any more.
As background for the present invention, the conventional interpolation technique is described below. FIG. 1 is a block diagram of a typical feedback-based transmission beamforming system.
Referring to FIG. 1, a transmitter includes an encoder and modulator 101, a weight multiplier 103, a plurality of transmit antennas 107-1 to 107-NT, and a weight generator 105. A receiver includes a plurality of receive antennas 109-1 to 109-NR, a MIMO decoder 111, a demodulator and decoder 113, and a codebook selector 115
In the transmitter, the encoder and modulator 101 encodes transmission data in a predetermined coding scheme and generates complex symbols by modulating the coded data in a predetermined modulation scheme. The weight generator 105, which manages a codebook database, generates weight vectors (or beamforming vectors) according to a codebook index fed back from the receiver. The weight multiplier 103 multiplies the complex symbols by the beamforming vectors and sends the products through the transmit antennas 107-1 to 107-NT.
In the receiver, signals received at the receive antennas 109-1 to 109-NR include added noise. The MIMO decoder 111 estimates a transmitted vector from the transmitter by decoding the vector of the received signals in a predetermined MIMO detection method. The demodulator and decoder 113 demodulates and decodes the estimated symbols, thereby recovering the original information data.
Meanwhile, the codebook selector 115 forms a channel matrix H by estimating channels using predetermined signals (e.g. pilot signals) received from the MIMO decoder 111 and detects optimum beamforming vectors based on the channel matrix H. A memory is utilized to store a codebook. The optimum beamforming vectors are created by operating beamforming vectors read from the memory with the channel matrix H. Then the indexes (or codebook indexes) of the selected beamforming vectors are fed back to the transmitter on a feedback channel. Since the transmitter also has the codebook, the receiver just feeds back the indexes of the beamforming vectors, thus decreasing the amount of feedback information. For example, if the codebook has eight beamforming vectors, the codebook indexes can be represented in three bits.
Without using any interpolation technique, the transmitter has to receive transmission beamforming vectors for all frames and all of N subcarriers to optimize link performance. Despite the codebook index-based feedback, this method requires a large amount of feedback. To solve the problem, interpolation is conventionally performed in frequency domain.
FIG. 2 illustrates beamforming vector interpolation techniques in a conventional transmission beamforming system. In FIG. 2, reference character (a) represents linear interpolation on a multidimensional sphere, and reference character (b) represents zero-order interpolation using the same transmission beamforming vector in each predetermined cluster.
The linear interpolation technique (a) feeds back an optimum beamforming vector w for every K subcarriers and calculates the transmission beamforming vectors of the in-between (K−1) subcarriers. As much uncertainty as a complex constant that exists in the optimum transmission beamforming vector itself and phase information θ is fed back along with the optimum beamforming vector to compensate for the uncertainty. Despite the advantages of a significant decrease in feedback information and a lesser amount of computational complexity in determining the feedback information at the receiver, the linear interpolation (a) adds to feedback information due to the transmission of the phase information.
The zero-order interpolation (b) in FIG. 2 groups total subcarriers into clusters each having K successive subcarriers and determines a transmission beamforming vector for each cluster. Thus, this interpolation is one-dimensional clustering. The receiver selects an optimum beamforming vector (which maximizes Signal-to-Noise Ratio (SNR) or channel capacity) for the K subcarriers of each cluster and feeds back the codebook indexes of the beamforming vectors of the clusters to the transmitter. This interpolation method requires a relatively high computational complexity in determining feedback information at the receiver, although decreasing the amount of feedback information.
The conventional interpolation techniques substantially reduce feedback information compared to a scheme that does not use interpolation. Conventional techniques, however, have the problem of great performance degradation in a time-varying channel environment. Referring to FIG. 2, assuming that feedback information is delivered in an mPth frame, a channel variation is not significant when the feedback information arrives. Therefore, performance is not degraded much. However, the channel variation becomes very serious just before the next feedback time instant. What is worse, the performance degradation caused by the channel variation occurs every time feedback is performed. A real system experiences a processing delay of at least one frame from channel estimation at the receiver to use of transmission beamforming vectors based on the channel estimation at the transmitter. During the processing delay, the channel changes. Accordingly, there is a need for a transmission beamforming technique to be robust against time-variant channels.